What type of data analytics is for decision support and decision automation?

Different types, types, and stages of data analysis have emerged due to the big data revolution. Data analytics is booming in boardrooms worldwide, promising enterprise-wide strategies for business success. But, what do these imply for businesses? Gaining the correct information, which produces knowledge, allows businesses to create a competitive edge, which is essential to companies successfully leveraging Big Data. The key purpose of big data analytics is to assist businesses in making better business decisions. Big data analytics should not be viewed as a one-size-fits-all solution. In addition, what sets the top data scientist or data analyst apart from the rest is their ability to recognize the various kinds of analytics that can be used to best help the company. The three most common types of analytics, descriptive, predictive, and prescriptive analytics, are interconnected solutions that help businesses make the most of their big data. All of these analytics approaches provide a unique perspective. In this article, we explore the three different types of analytics -Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics - to understand what each type of analytics delivers to improve an organization’s operational capabilities.

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Table of Contents for Types of Analytics

Thomas Jefferson believes that all analytics are not created equal.

Big data analytics helps a business understand the requirements and preferences of a customer so that companies can increase their customer base and retain the existing ones with personalized and relevant offerings of their products or services. According to IDC, the big data and analytics industry is anticipated to grow at a CAGR of 26.4%, reaching $41.5 billion by the end of 2018. The big data industry is snowballing due to various applications like smart power grid management, sentiment analysis, fraud detection, personalized offerings, traffic management, etc., across myriad industries. After the organizations collect big data, the next important step is to get started with analytics. Many organizations are unsure where to begin, what kind of analytics can nurture business growth, and what all these various kinds of the analytics mean. Let's explore the different kinds of analytics and the value they bring into any business -

Descriptive Analytics

90% of organizations today use descriptive analytics, the most basic form of analytics. The simplest way to define descriptive analytics is that it answers the question “What has happened?”. This type of analytics analyses the data coming in real-time and historical data for insights on how to approach the future. The main objective of descriptive analytics is to find out the reasons behind precious success or failure in the past. The ‘Past’ here, refers to any particular time in which an event had occurred and this could be a month ago or even just a minute ago. The vast majority of big data analytics used by organizations falls into descriptive analytics.

A company learns from its actions in the past to predict future events. Descriptive analytics is used when an organization seeks to analyze and define its overall performance at a high level.

Dr. Michael Wu, chief scientist of San Francisco-based Lithium Technologies, describes descriptive analytics as -“The simplest class of analytics, one that allows you to condense big data into smaller, more useful nuggets of information.” 

Descriptive analytics are based on standard aggregate functions in databases, which require knowledge of basic school math. Most of the social analytics are descriptive analytics. They summarize certain groupings based on simple counts of followers, likes, posts, fans are mere event counters. These metrics are used for social analytics like average response time, the average number of replies per post, %index, number of page views, etc.

A business's results from the webserver using Google Analytics tools are the best example of descriptive analytics. The results assist in determining what exactly occurred in the past and determining if a promotional effort was effective or not based on simple metrics such as page views.

It is the simplest form of analytics, and it describes or summarises the existing data using existing business intelligence tools. Therefore, it becomes easier to understand what is going on or what has happened. The main techniques used here are data mining and data aggregation.

Descriptive analytics involves using descriptive statistics such as arithmetic operations on existing data. These operations make raw data understandable to investors, shareholders, and managers. Thus, the clarity of data can help individuals and industries analyze key areas.

Using historical data, companies analyze consumer behaviors and engagements with their businesses. For this reason, it's helpful in service improvement and targeted marketing. It's used to identify and address the areas of strengths and weaknesses, and this type of analytics importantly uses tools like MS Excel, MATLAB (MaTrix LABoratory), STATA, etc.

Many learning systems use descriptive analytics for analytical reporting. They measure learner performance to ensure targets and training goals are fulfilled. A few examples of how you can use descriptive analytics in the e-learning industry include- 

• Analyzing assessment grades and assignments

• Tracking the use of learning resources

• Comparing the test results of learners

• Analyzing the time taken by the learner to complete the course

Descriptive Analytics Use Cases

• Use of social media and engagement data (Facebook and Instagram likes)

• Summarising past events such as marketing campaigns sales.

• Collating survey results

• Reporting general trends

Descriptive Analytics Example - Case Study Of Kankor In Afghanistan

High school graduates in Afghanistan need to pass the NUEE (National University Entrance Exam).

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This exam helps in mining educational data and techniques for educational enhancements. In-depth descriptive analytics of the exam helps improve the current structural models in Afghanistan.

A research paper by Cornell University explains the in-depth analysis of Kankor for this context. This research paper contributes to introducing the importance of data as an asset. It collects and produces data effective for research studies using descriptive analytics. Arithmetic operations on the data helped convert it to useful information. As a consequence, it paved the way for in-depth descriptive analysis.

Predictive Analytics

The subsequent step in data reduction is predictive analytics. Analyzing historical trends and patterns can accurately inform a business about what could happen in the future. This facilitates the establishment of realistic goals for a company, strategic planning, and expectations handling. Predictive analytics is used by businesses to study the data and ogle into the crystal ball to find answers to the question, “What could  be the future outcome based on previous trends and patterns?”

Dr. Michael Wu, chief scientist of San Francisco-based Lithium Technologies, said -"The purpose of predictive analytics isn’t to tell you what will happen in the future. It cannot do that, and none of the analytics is capable of doing that. Predictive analytics can only forecast what might happen in the future because all predictive analytics are probabilistic."

Organizations collect contextual data and relate it with other customer user behavior datasets and web server data to get real insights through predictive analytics. Companies can predict business growth in the future if they keep things as they are. Predictive analytics provides better recommendations and more future-looking answers to questions that BI cannot answer.

Predictive analytics helps predict the likelihood of a future outcome by using various statistical and machine learning algorithms. Still, the accuracy of predictions is not 100%, as it is based on probabilities. Algorithms take data and fill in the missing data with the best possible guesses to make predictions. This data is pooled with historical information present in the CRM systems, POS Systems, ERP, and HR systems to look for data patterns and identify relationships among various variables in the dataset. Organizations should capitalize on hiring a group of data scientists in 2016 who can develop statistical and machine learning algorithms to leverage predictive analytics and design an effective business strategy.

Predictive analytics can be further categorized –

1. Predictive Modelling –What will happen next, if?

2. Root Cause Analysis-Why this happen?

3. Data Mining- Identifying correlated data. 

4. Forecasting- What if the existing trends continue?

5. Monte-Carlo Simulation – What could happen?

6. Pattern Identification and Alerts –When should action be invoked to correct a process.

Sentiment analysis is the most common kind of predictive analytics. The learning model takes input in the form of plain text, and the output of the model is a sentiment score that helps determine whether the sentiment is positive, negative, or neutral.

Predictive analytics is used by companies such as Walmart, Amazon, and other retailers to recognize sales patterns based on customer buying patterns, forecast consumer actions, forecast stock levels, and predict the sales revenue at the end of each quarter or year. The best example where predictive analytics finds great application is generating the credit score, and a credit score helps financial institutions decide the probability of paying credit bills on time.

Big data might not be a reliable crystal ball for predicting the exact winning lottery numbers. Still, it definitely can highlight the problems and help a business understand why those problems occurred. Companies can use the data-backed and data-found factors to create prescriptions for the business problems that lead to realizations and observations.

Prescriptive analytics is the next step of predictive analytics that adds the spice of manipulating the future. Prescriptive analytics advises on possible outcomes and results in actions that are likely to maximize key business metrics. It uses simulation and optimization to ask, “What should a business do?” 

Prescriptive analytics is an advanced analytics concept based on –

• Optimization that helps achieve the best outcomes.

• Stochastic optimization helps understand how to achieve the best outcome and identify data uncertainties to make better decisions.

Simulating the future, under various sets of assumptions, allows scenario analysis - which, when combined with different optimization techniques, allows prescriptive analysis to be performed. The prescriptive analysis explores several possible actions and suggests actions depending on the combined results of predictive and descriptive analytics of a given dataset.

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Prescriptive analytics is a combination of data and various business rules. The prescriptive analytics data can be internal (within the organization) and external (like social media data). Business rules are preferences, best practices, boundaries, and other constraints. Mathematical models include natural language processing, machine learning, statistics, operations research, etc.

Prescriptive analytics is comparatively complex in nature and many companies are not yet using them in day-to-day business activities, as it becomes difficult to manage.If applied effectively, predictive analytics can have a significant impact on business growth. Large-scale organizations use prescriptive analytics to schedule the inventory in the supply chain, optimize production, etc. to optimize the customer experience.

The Aurora Health Care System saved $6 million per year by cutting re-admission rates by 10% through prescriptive analytics. In the healthcare industry, prescriptive analytics can be used to improve medication production, find the most suitable patients for clinical trials, etc.

Diagnostic Analytics

Diagnostic analytics is the application of analytics to internal data to determine the "why" behind what occurred. This kind of analytics is used by businesses to get an in-depth insight into a given problem, provided they have enough data at their disposal. Diagnostic analytics helps identify anomalies and determine casual relationships in data. For example, eCommerce giants like Amazon can drill the sales and gross profit down to various product categories like Amazon Echo to find out why they missed their overall profit margins. Diagnostic analytics also find applications in healthcare for identifying the influence of medications on a specific patient segment with other filters like diagnoses and the prescribed medicine.

Understanding Predictive and Descriptive Analytics

A lioness hired a data scientist (fox) to help find her prey. The fox had access to a rich DataWarehouse, which consisted of data about the jungle, its creatures, and events happening in the jungle.

On its first day, the fox presented the lioness with a report summarizing where she found her prey in the last six months, which helped the lioness decide where to go hunting next. This is an example of descriptive analytics.

The fox estimated the probability of finding prey at a specific place and time using advanced ML techniques. This is predictive analytics. Also, it identified routes in the jungle for the lioness to minimize her efforts in finding her prey. This is an example of Optimization.

Finally, based on the above models, the fox got trenches dug at various points in the jungle so that the prey got caught automatically. This is Automation.

Descriptive Analytics -> Predictive Analytics / Optimization -> Automation. This is the AnalyticsLifeCycle.

As an increasing number of organizations realize that big data is a competitive advantage, they should ensure that they choose the right kind of data analytics solutions to increase ROI, reduce operational costs, and enhance service quality.

Diagnostic Analytics includes techniques such as data discovery, drill-down, data mining, and correlations. Diagnostic analytics primarily uses likelihoods, probabilities, and distribution of outcomes.

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FAQs on Types of Analytics

What are the four types of analytics?

The four types of data analytics are- Descriptive, Diagnostic, Predictive, and Prescriptive.

• Descriptive analytics examines historical events and tries to find specific patterns in the data.
• Diagnostic analytics- It's a type of advanced analytics that looks at data or content to figure out what caused an event to happen.
• Predictive analytics is a sort of advanced analytics that aims to answer the question "What is likely to happen?" using data and information.
• Prescriptive analytics is a type of data analysis that provides recommendations for possible outcomes. 

What types of analytics are applied in the injury analysis?

You can apply prediction and reporting analytics to categorize injuries into different types depending on factors such as the affected body part, the start and end dates, etc.

What are the 4 main types of data analytics?

How does data analytics relate to decision support?

Which type of analytics is a statistical method used to generate recommendations and make decisions based on the?

What are the 3 areas of analytics that can contribute to decision making?